Dynamic virtual private network provisioning from multiple cloud infrastructure service providers

The Cloud infrastructure service providers currently provision basic virtualized computing resources as on demand and dynamic services but there is no common framework in existence that allows the seamless provisioning of even these basic services across multiple cloud service providers, although this is not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. We present a solution idea which aims to provide a dynamic and service oriented provisioning of secure virtual private networks on top of multiple cloud infrastructure service providers. This solution leverages the benefits of peer to peer overlay networks, i.e., the flexibility and scalability to handle the churn of nodes joining and leaving the VPNs and can adapt the topology of the VPN as per the requirements of the applications utilizing its intercloud secure communication framework

Secure communication using dynamic VPN provisioning in an Inter-Cloud environment

Most of the current cloud computing platforms offer Infrastructure as a Service (IaaS) model, which aims to provision basic virtualised computing resources as on-demand and dynamic services. Nevertheless, a single cloud does not have limitless resources to offer to its users, hence the notion of an Inter-Cloud enviroment where a cloud can use the infrastructure resources of other clouds. However, there is no common framework in existence that allows the srevice owners to seamlessly provision even some basic services across multiple cloud service providers, albeit not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. In this paper we present a novel solution which aims to cover a gap in a subsection of this problem domain. Our solution offer a security architecture that enables service owners to provision a dynamic and service-oriented secure virtual private network on top of multiple cloud IaaS providers. It does this by leveraging the scalability, robustness and flexibility of peer- to-peer overlay techniques to eliminate the manual configuration, key management and peer churn problems encountered in setting up the secure communication channels dynamically, between different components of a typical service that is deployed on multiple clouds. We present the implementation details of our solution as well as experimental results carried out on two commercial clouds

A Mediation Framework for Mobile Web Service Provisioning

Web Services and mobile data services are the newest trends in information systems engineering in wired and wireless domains, respectively. Web Services have a broad range of service distributions while mobile phones have large and expanding user base. To address the confluence of Web Services and pervasive mobile devices and communication environments, a basic mobile Web Service provider was developed for smart phones. The performance of this Mobile Host was also analyzed in detail. Further analysis of the Mobile Host to provide proper QoS and to check Mobile Host's feasibility in the P2P networks, identified the necessity of a mediation framework. The paper describes the research conducted with the Mobile Host, identifies the tasks of the mediation framework and then discusses the feasible realization details of such a mobile Web Services mediation framework.Comment: Proceedings of 2006 Middleware for Web Services (MWS 2006) Workshop @ 10th International IEEE EDOC Conference "The Enterprise Computing Conference", October 16, 2006, pp. 14-17. IEEE Computer Societ

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin